System for collecting, analyzing, and transmitting information relevant to transportation networks

ABSTRACT

When individual persons or vehicles move through a transportation network, they are likely to be both actively and passively creating information that reflects their location and current behavior. In this patent, we propose a system that makes complete use of this information. First, through a broad web of sensors, our system collects and stores the full range of information generated by travelers. Next, through the use of previously-stored data and active computational analysis, our system deduces the identity of individual travelers. Finally; using advanced data-mining technology, our system selects useful information and transmits it back to the individual, as well as to third-party users; in short, it forms the backbone for a variety of useful location-related end-user applications.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/429,186 filed on Mar. 23, 2012, which is a continuation of and claimsthe benefit of priority under 35 U.S.C. §120 to U.S. patent applicationSer. No. 09/690,046 filed on Oct. 16, 2000 entitled “System ForCollecting, Analyzing, and Transmitting Information Relevant toTransportation of Networks,” which claims the benefit of U.S.Provisional Patent Application No. 60/159,772, filed 15 Oct. 1999,titled “System for Collecting, Analyzing and Transmitting InformationRelevant to Transportation Networks” and is hereby incorporated byreference in its entirety.

BACKGROUND OF THE INVENTION

When individual persons or vehicles move through a transportationnetwork, they are likely to be both actively and passively creatinginformation that reflects their location and current behavior. In thispatent, we propose a system that makes complete use of this Information.First, through a broad web of sensors, our system collects and storesthe full range of information generated by travelers. Next, through theuse of previously-stored date, and active computational analysis, oursystem deduces the identity of individual travelers. Finally, usingadvanced data-mining technology, our system selects useful Informationand transmits it back to the individual, as well as to third-partyusers; in short, it forms the backbone for a variety of usefullocation-related end-user applications.

BRIEF SUMMARY OF THE INVENTION

It Is our contention that such information is even more valuable when itis gathered and stored centrally. This allows for the application ofadvanced data analysis techniques that can detect patterns and formconnections across the data sets, which may be of great value both tothe original traveler as well as to interested third parties. Forexample, it may be that the congestion of the harbor (i.e. ship traffic)has a significant impact on the travel times of the commuter boat. Oursystem would detect this connection by correlating the ferry's arrivaland departure times with the harbor radar data. Using this information,a real-time navigational application could then allow the ferry operatorto make precise predictions for the estimated time of arrival, given thecurrent state of the harbor traffic.

As outlined In a previous, patent, LEIA (Location Enhanced InformationArchitecture) provides a framework for the collection, analysis, andretransmission of relevant data. This is a very general architecturewhich can be broken down into, the following steps (FIG. 1 provides aschematic of this process);

1. Sensors acquire signals from an individual user

These signals include everything that can be used to identify andgeographically locate an individual, be they from Active Badges,cellular phones, motion detectors, EZ-Pass toll-booth devices,interactions with a computer workstation, etc. Such signals may beactively or passively generated.

2. Sensors emit Location Identifiers (LID)

Having detected an individual, sensors transmit special codes, calledLIDs, to a central server. LIDS include location, time stamp, and signalinformation.

3. Secure System translates LIDs to User Identifiers (UID)

Content of LID used to infer user's identity; the UID that is chosen maybe pseudonymous (to protect privacy at this stage). At this point thesystem has both locational/behavioral information (contained in theLIDs) as well as information on a user's identity.

4. UIDs used to access personal profile data.

This may be done by means of a proxy server, in the case of pseudonymousUID.

5. User Identity, Location, and Personal Profile Data Used in Choice ofMost Relevant Information Set

The most relevant information depends on the nature of the particularapplication, and is determined by the LID and profile data connected tothe UID. Generally, the LID contains information about the current stateof the individual, whereas the UID links to past information (the“history” or “profile of the” individual). Applications will generallymake use of the individual user's profile, other users' profiles, andbackground information relevant to the domain (e.g. weather or trafficconditions in the locale of the user).

6. Most Relevant Information Set Delivered to Individual or Third-partyUser.

We adapt this general architecture to be of particular use to travelers(be they people or vehicles) involved with transportation systems (road;air, sea, or intermodal), and refer to it as LEIA-TR (for LEIA appliedto TRansport).

What follows is a detailed description of how this adaptation isaccomplished, using the particular example of automobiles. Althoughspecific types of sensors and end-user applications are mentioned, thesecan always be enhanced, added-to, or replaced. The importance of thedescription is in showing how LEIA can be used as a generaldata-collection and analysis architecture relevant to transport systems.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the invention are setforth in the appended claims. The invention itself however, as well as apreferred mode of use, further objects and advantages thereof, will bestbe understood by reference to the following detailed description of anillustrative embodiment when read in conjunction with the accompanyingdrawings, wherein:

FIG. 1 is a schematic of the Location Enhancement InformationArchitecture;

FIG. 2 is a simplified schematic description of the use of the. LEIAsystem.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 2 gives a simplified schematic of the description that folks (anddoesn't include all the details given in the description).

As applied to automobiles, LEIA-TR would adapt to the generalized LEIAframework in the following way:

1. Sensors acquire signals (generated actively and passively, bothinternal and external to vehicle)

-   -   Global Positioning Satellite (GPS) receiver    -   Vehicles' current speed and directional acceleration    -   Automated recognition of license-plate tags by roadside cameras    -   EZ-Pass use at toll booths    -   Vehicles' biometrics sensors    -   Lo-Jack transmissions. (normally used as locational beacon in        case of car theft)    -   Particular settings of car's near-view mirror, seat belt        lengths, seat positions.    -   Logs and content pertaining to:        -   e-mail        -   telephone calls        -   web browsing        -   personal calendar agents            (These are communications generated either in-vehicle, or at            home before trip)    -   Current traffic patterns    -   Day-of-year, day-of-week, current time and current weather.    -   Credit card, ATM, or public telephone transactions.

2. Sensors Emit Location identifiers (LID)

LEIA-TR dies at the center of a web of sensors; when any of these aretriggered by a traveler's passage, they transmit the information they'vegathered to a central server. These transmissions, which are sent usinga standard protocol, are termed Location Identifiers (LIDs).

It also is important to note that many of the sensors, such as thosedetecting biometrics, GPS coordinates, and vehicle driving behavior(e.g. pressure on the pedals, speed of turns, etc.), are located in thecar itself, which transmits LIDs to a central server using wirelesscommunication technology (e.g., the iridium satellite telephone). When aGPS receiver is installed in a vehicle, the geographical coordinates canbe transmitted in conjunction with the LID's, thereby giving LEIA-TR avery accurate real-time estimate of the vehicle's location.

3. Secure System Uses LIDs to Deduce User Identifiers (UID)

Although the specific identity of an individual driver might not beknown, LEIA-TR can make use of the available signals, as well as adatabase linked to the vehicle, to deduce the identity of the individualbehind the wheel (whether it be the person's true identity or simply apseudonym, depending on security settings). This non-parametric processis described in detail below.

Note on Computational Strategies

Although it is possible to naively dump all available sensor inputs intoa computational “black box”, the high dimensionality of the input spacecan potentially render even the largest data set sparse, reducing theeffectiveness of LEIA-TR's inferences. A better strategy is to determinewhich inputs have the greatest effect on different outputs, constructingappropriate statistical inferences for each set.

There are, of course, a multitude of non-parametric techniques that canbe used for classification; the power of a particular technique (be itnearest neighbor or a neural network) often depends on the particularnature of the data being examined. The following discussion will outlinegeneral computational approaches, but it should be understood that theparticular algorithms used to implement them are fairly interchangeable,and might well depend on the particular nature of the data examined. Forsets of data that happen to be particularly complex (i.e. nonlinear), itmay be necessary for a data analyst to identify and focus on the mostrelevant subsets of the data.

Inferring the Identity of the Driver

A certain subset of the input data I, call it I_(D), will be most usefulfor establishing the identity of a vehicle's driver (and perhaps otherpassengers). D_(i) (where i indexes the licensed drivers in the family).This should be fairly straight-forward to establish; indeed, many luxurycars today have keys that allow them to distinguish individual drivers,automatically reconfiguring such details as the angle of the seat andtilt of the rearview mirror upon insertion of the key. Of course, thisdata could be supplemented by biometric readings (fingerprints,voiceprints) and physical behavior of the driver (foot pedal pressure,average speed, and the sharpness of turns). If this information were tobe linked to the vehicle's wireless LID transmissions, LEIA-TR wouldhave little trouble distinguishing, drivers.

Because such data should give a fairly unambiguous signal about theidentity of the driver, the classification problem is straight-forwardand could be economically handled by a rule-base, which slices the inputspace into fairly broad regions that correspond to different categories.An example of such a rule would be:

Given two drivers in the family (D₁=90-year-old grandmother,D₂=17-year-old male),

Rule X. IF [(Speed>40 mph) AND (Radio_Music_Genre==Rock)] THEN(Driver=D₂)

There are numerous ways to perform the induction of such rules, forexample by genetic programming or by estimating a non-parametricregression tree, both of which are well-understood and documented in theliterature.

The resulting rule base can be thought of as a function r I_(D→)D.

4. UIDs used to access personal profile data.

Having derived a pseudonymous UID for the individual behind the wheel ofthe vehicle, LEIA-TR connects to a proxy server containing:

-   -   Database of individuals' past driving behavior (destinations,        cruising speeds, etc.).    -   Database of individuals' demographic profiles    -   Database of individuals' past selection of information content        (i.e., what radio stations did they listen to en-route?)    -   Computerized road maps        (5,6) Most appropriate set of information chosen and delivered        to driver or to third-party user.

In the final stages, information is processed and delivered, as definedby the application for which LEIA-TR has been configured. Sampleapplications, and users of interest, are listed below.

Application A: Personalized Information Delivery, for Driver (orPassengers) of Automobile

Equipped with the proper sensors, given access to certain databases, andloaded with appropriate algorithms, LEIA-TR forms the foundation for anintelligent system capable of inferring drivers' identities, predictingtheir future locations, and predicting the content of the informationthey'd like delivered to their in-car computers/viewing/listeningsystems. In short, LEIA-TR can be used as a wireless automotive “push”technology.

For example, one could imagine a commuter in California who on his wayto work would like to get the latest stock quotes on Microsoft, checkhis offices voicemail, and hear the news from Germany, interspersed withlocal traffic reports. Although no radio station might provide thisparticular combination of programming, it could easily be supported byLEIA-TR. Linked to the user's car via a wireless connection, LEIA-TRwould either be handed the driver's identity code (signaled by the caritself), or use the driver's behavior, biometrics, and EZ-Pass code toinfer it. Taking into account the driver's past trips, personal calendaragent, date and time of day, LEIA-TR could predict the route that hewill follow. Using information on current traffic patterns (as well asknowledge of the driver's preferred driving speed), LEIA-TR couldestimate when the driver would be closest to various transmittingservers along his route. These would be loaded (pre-cached) withappropriate chunks of programming and traffic reports so that the driverwill be provided with a constant stream of data as he goes to work.Economies of scale would also come into play; determining that manydrivers are interested in the day's weather report, for example, LEIA-TRcould load the report into a few servers in proximity to the mostheavily-traveled traffic arteries. Each driver's programming would thenbe arranged so that the weather report would be loaded as they pass oneof these central servers. Such coordination would require massiveamounts of computation, but would be quite feasible using modemstatistical techniques, and would certainly maximize the effectivenessof LEIA-TR's pre-caching technology.

Of course, pre-cached data need not pertain exclusively to publicinformation streams. If a passenger is accessing the Web, email, orvoice mail, pertinent files can be transferred from remote file caches(e.g. those on his personal computer at home) to nearby servers.

Inferring Location, Current and Future

Once the driver's Identify has been inferred, LEIA-TR needs to be ableto 1) predict current (if GPS LIDs are not available) and futurelocation of the automobile, and 2) predict the driver's informationneeds. It is a much more challenging task to infer the current (and topredict the future) location of the vehicle, since it is not beingconstantly monitored: we might glimpse it at a toll-both as its EZ-Passregisters, or we might be handed a GPS code when the driver requests adigital road map. LEIA-TR must infer, from these irregular samples, theroute of the vehicle. The appropriate portion of the input space, I_(L),would include samples of communications, map data, GPS signals, EZ-Passsignals, Lo-Jack signals, automated license-tag readers, time and datestamp, weather conditions, and traffic conditions.

As disclosed in co-pending patent application entitled “LocationEnhanced Information Architecture” the location information may beinferred from the relative signal strengths of a probable user's roamingsignal as detected by two or more nearby cellular transmitter/receiver“towers.

LEIA-TR also has access to a database containing past routes andconditions for driver D. Since the vehicle would have been observed atdifferent times and at different locations, the raw database might wellbe “patchy”, in that some trips might only have a few location datapoints. One way to normalize this data would be the following: Assumethat the car sends a signal to LEIA-TR both upon ignition and upon beingshut off. Every trip would then consist of L_(o) (location whenstarted), LT (location when parked), and most likely a series of L_(t)(where for time t, 0<t<T). Using this information and a digital roadmap, LEIA-TR could then infer the full route followed. For example, ifthe starting location point was at the home, the ending point was at theoffice, and three locational signals fell on a superhighway, one couldinfer that the full route took the vehicle from the home to the on-ramp,along the highway to the off-ramp, then to the office. Havingreconstructed the full path of each of these routes, it would then bepossible to sample them at regular intervals, so that every trip in thedatabase would be described by points on the same grid. In addition tostandardizing the way we describe paths, this approach fills in the gapsfor trips in which very few location readings were taken. Note that thegrid can be defined by a distance (such as a half-mile interval), ormore usefully, the position of transmitters that serve out informationto passing cars. These gridded locations along a route can be thought ascheckpoints.

Even after the location-points are standardized, this route historydatabase will be extremely large, since it will contain a detailedrecord of every trip made by the car: the driver's identity, passengers'identities, various state variables (weather, time, driving patterns),and geographical paths driven. Some effort needs to be made to reducethe input space's dimensionality; this could be done through suchmethods as Principal Component Analysis, which could determine (for acommuter, e.g.) that the day-of-week and time-of-day are the mostimportant variables needed for characterizing different routes taken bythe car.

The raw route data is thus boiled down into a final, more compact,format. An entry in LEIA TR's database would then be of the form:(current-state (time, date, weather, etc. in compact form), L_(o)(starting location), . . . , L_(n) (nth location), . . . , L_(T) (finallocation)).

In regular operation, LEIA-TR will maintain a similar vector, y, for thecurrent trip: the state is boiled down to the compact form (for example,casting 9:15 am→Morning), and the previous location checkpoints arerecorded.

At this point, there are a variety of standard nonparametric methodsthat can be brought to bear. Given the current state y of the automobile(occupants, time of day, day of week, etc.), and given the database ofpast routes taken by this automobile, which can be correlated with theformer, it is possible to assign probabilities to the possibledestinations for the current route being taken.

Note that many complexities can be added to this analysis. Using similarmethods, we could, for example, generate a conditional probabilityP(L_(t+n)|I_(t), D, y) for the nth future checkpoint location (whereI_(t) represents the state information at time t). Thus, as the currenttrip's state vector y is updated, each checkpoint in the surroundingarea can be assigned a probability that it will be passed by the targetvehicle. LEIA-TR might then pre-cache data in those several locationswith the highest probabilities.

By noting both the current traffic conditions, projected route, and D'saverage driving speed, it would be possible to predict the time at whichthe target vehicle will pass future location checkpoints, allowingLEIA-TR to optimize pre-caching. Among the many applications ofpre-caching could be targeting of advertising at strategicallypre-designated location(s) of the mobile user or, providing the userwith personalized physical location relevant sites of interest orretailers (e.g., offering a user desired product(s), the targetingconditions for which, could be (previously) manually determined orperformed automatically (as detailed or referenced in the parent case).Or manually approved criteria or objects of future anticipated proximitycould be automatically recommend then approved by the user for automaticnotification (then the object(s) (or objects relevant to the recommendedcriteria) come into physical proximity to the user. (E.g., some objectsor criteria may not be determined to be definitively of high enoughpriority to the user's preferences to justify active notification.

An extra layer of Intelligence could be applied to situations in which anovel route is being undertaken (indicating, for example, across-country road-trip). LEIA-TR would then make use of phone ande-mail communications logs (looking for location keywords), as well asthe driver's personal calendar agent, recent book purchases onAmazon.com, and so forth to determine the target's final destination.Intermediate checkpoints would then be interpolated.

Of course, those automobiles equipped with on-board navigational systems(such as NaviStar) would very likely have been programmed by the driver,in advance, with specific navigational goals. Little inference wouldneed to be done, in such cases, as long as the vehicle remained ontrack.

Inferring Information Content

Finally, given an appropriate portion of the input space I_(c), theInferred driver D, and most likely path L₁, . . . , L_(final), it shouldbe possible to predict the content of the information that the driverwill request. In many cases, this should be straight-forward, and couldbe implemented by another rule-base. Assuming that I_(c) includes thetime-of-day, day-of-week and the driver's history of informationrequests, it should be possible to capture the patterns of informationusage for typical commutes (news in the morning, classical music on theway home) or weekend activities (surf updates on the way to the beach)The parent case (LEIA) discussed pre-caching “panels” such as this inanticipation of the driver's entering a particular region for which itis expected that higher resolution displays and more detailedinformation will be needed.

The driver, of course, has the ability to directly control theinformation he receives, of course, and can send LEIA-TR explicitinstructions for information at the touch of a button (a driver mightnot have had time to finish the New York Times over coffee at breakfast,and could request the articles be read to him in the car). Suchexceptions to the non-parametric generation of likelihoods for contentinterest would be hard-wired into the rule-base, and could take theform:

Rule 1. IF (emergency button pressed) THEN (link cell-phone to 911)

Other hard-wired exceptions might include road-trips; the rule-base,recognizing a novel travel path, could hand off control of theinformation content prediction routine to a nationwide travel systemmaintained by AAA, for example. This could display road maps,information about tourist attractions, locations of gas stations (whenfuel runs low) and fast food restaurants (when lunchtime approaches).

More sophisticated users could also be given access to thecontent-delivery models directly; they could examine and modify thevarious thresholds that determine information-delivery in fuzziersituations.

Further Examples of Personalized Information Delivery Applications

i) Targeted Advertising

If the user has a profile desirable to a particular advertiser,autonomous user-side agents could negotiate certain terms and conditionsfor the packaging of advertisements with the content to be delivered(The parent case expounds upon the issued patent system for Generationof User Profiles by discussing anonymous or pseudonymous user profileswhich can be queried and accessed easily by advertisers who pay or cannegotiate terms for rights to deliver ads to users).

The concessions by the user could include but are not limited to theuser allowing the advertiser to deliver ads (or other content) relevantto the user's preferences via the in-car display (e.g., interspersedwith other content which the user has selected for consumption) oraudibly via the automobile's radio speaker system (either during a radioprogram's commercial breaks or otherwise) or via an electronicbillboard. In each of these cases, if the advertising is relevant to thepresent location of the user, it may be preferentially delivered atthose appropriate times) and via the delivery medium most accessible tothe user, most opportune for the advertising message or otherwisepreferred by the advertiser though ultimately subject to the term andconditions acceptable to the user.

ii) Personalized. Maps

It would be possible to provide on-board electronic map displays formobile users which would be customized according to the users'interests. The maps could be programmed by the user to reveal certaincategories of information, such as restaurants, nightspots, shopping (ora particular desired product), points of tourist or historical interest,etc.

The user can tune the system to be more or less selective in displayingpersonally relevant items, with various filtering options. For example,it might reveal only those restaurants which are low priced, ethnic, andopen after 9:00 pm. The system may also notify the user as s/he comeswithin physical proximity of desirable sites.

iii) User-to-User Meetings

Such maps could also reveal the physical locations of other individualswith whom the user may be interested in scheduling meetings or evenestablishing a first-time contact (such a contact would be brokeredthrough autonomous agents capable matching users having similar personalprofiles). The parties personal schedules in combination with currentand predicted future locations could be used to notify users, revealmutual geographic locations, and even suggest (and direct users to)appropriate venues.

iv) Real-Time Traffic Reports

A simple but useful application of LEIA-TR, would evolve the real-timetransmission of drivers' location information to a regionaltraffic-reporting bureau. This information would be analyzed for thelocations of current and near-future traffic congestion, which wouldthen be broadcast back to the drivers in the region. On-board navigationsystems would then suggest alternate routes to individual drivers, giventheir current positions and predicted destinations.

Application B: In-Car Warning System, for Driver.

Because LEIA-TR has access to data both inside and local to a givenautomobile, it could be used to support a number of devices to improvesafety for both drivers and passengers.

In addition to the sensors and communications devices already installedin a vehicle, it would be possible to add a small computer capable ofcommunicating verbally with the driver. Being connected to the centralLEIA-TR server via the wireless communications system, this device wouldbe able to override most other in-car information systems to verballydeliver important safety messages to the driver, regarding events bothwithin and external to the vehicle.

In the first case, the device might notice that the car is being driventoo fast, conditional on the location, weather, traffic flow, and timeif several accidents have occurred on the given stretch of road undersimilar conditions the driver would be informed of this fact.Furthermore, communications with personalized agents at the driver'shome might reveal that the driver is lacking sleep or has consumedalcohol, both of which would give the safety device grounds fordecreasing the threshold used to determine the need for a verbalwarning. Conversely, if a driver is going rather fast, but currentconditions are extremely good (straight road, no traffic, perfectvisibility, warm and dry weather), the device could increase its warningthreshold to avoid needlessly bothering the driver.

In addition to being linked to LEIA-TR's statistical databases, thesafety device would have access to observations of current roadconditions. Thus, given that a slippery patch of ice or traffic accidenthas been observed two miles ahead of the driver's current position, andwhich fail within the driver's predicted path, LEIA-TR could verballywarn the driver of the oncoming obstacle and perhaps provide analternate route. It is conceivable that in the future, automobilesthemselves would be mounted with miniature weather stations that wouldfeed back into LEIA-TR. Thus, if a few cars encountered icy conditionson a certain stretch of road, it would take very little time for warninginformation to be sent to all vehicles heading for that location.

Such dynamically available weather information emitted from so manyclosely situated sources would also be valuable to enhance real timeweather prediction models; e.g., if conditions producing ice, such asfreezing rain, dense fog, heavy rain or hail, etc., are imminentlylikely to occur based upon weather patterns in the immediate vicinity,local warning could be issued for the imminent possibility of suchconditions.

Data from front-mounted infrared camera systems are already of use inenhancing the real-time visual capabilities of drivers. In addition towarning of such conventional dangers as pedestrians or deer in the road,a LEIA-TR enabled vehicle could add other information to the heads-updisplay to enhance the safety of the driver. For example, notablydangerous curves in the road, or recently-detected patches of ice couldbe highlighted. Or, the user could be alerted to the presence of ahigh-risk driver, (identified by previous criminal records and currenterratic driving). Anything deemed dangerous or worthy of attention couldbe “painted” on the windshield through the heads-up display. Especiallyuseful during periods of low visibility, such a system would warn thedriver of obstacles and recommend evasive strategies. Multiple vehiclesequipped with LEIA-TR could automatically exchange locational data,thereby allowing for coordinated movement during such periods.

Finally, the system could war the driver when he is in danger of fallingasleep at the wheel (and suggest courses of action, such as the locationof the next motel or vendor of coffee); sleepiness could be inferredafter long periods of non-stop driving, and by cameras mounted insidethe vehicle capable of monitoring the driver's position and behavior(iris scanning is a useful way of detecting alertness).

Application C: Emergency Notification on System, for Police

Once an accident has occurred, victims frequently rely on passers-by toinform the proper authorities. If the accident has taken place in aremote location or during a heavy snow-storm, the victims stand a goodchance of not being helped in time. If LEIA-TR's in-car system is stilloperational, it could use the car's communications system to send anautomated call-for-help accompanied by the car's GPS coordinates, giventhat a certain number of anomalies have been detected (a violentacceleration or impact was recently registered, the car is positioned onits side or back, the driver is not responding to verbal pages, thevehicle is not moving, etc.).

In some instances, it would be advantageous for those passers-byqualified to provide emergency or medical assistance to be notified of anearby motorist in need of help. While sending out a call for help,LEIA-TR could assess the severity of an accident (using previouslysuggested input variables) and determine whether qualified (and willing)individuals capable of providing emergency or medical help are in thevicinity.

In extreme cases, the in-car electronics may have been destroyed, aswell. However, because LEIA-TR monitors the positions of all its users(via communicated GPS location stamps), the last known coordinates of avehicle could still be instrumental in the search by authorities for amissing driver.

Application D: Traffic Flow Analysis, for Highway Engineers

In recent years, state highway departments have become much moresophisticated in their management of traffic flow, notably by usingtollbooth systems able to dynamically adjust prices. Given that amorning rush hour is going to clog inbound lanes, for example, it ispossible to temporarily charge drivers higher tolls for the use ofprincipal arteries at critical times. This encourages drivers to travelvia alternative routes, or to shift their commuting schedule away frompeak hours. In a sense, the flexible tolls act as a market force thatdisperses incoming traffic across multiple routes and times, lesseningoverall traffic flow pressure.

The calibration of such a system is not trivial, however, and in somecases may actually increase problems if traffic flows don't change inthe manner predicted. LEIA-TR provides a solution to this.

Located at the center of a complex web of inputs, LEIA-TR is able torecord, in precise detail, the timing and direction of traffic flow, theestimated number of passengers, the types of vehicles, the state of theweather, the occurrence of traffic accidents, and overall roadconditions. First of all, this information is highly useful forstate-of-the-art traffic prediction models—given the time, date,weather, road conditions, and the state of nearby roads, LEIA-TR canefficiently forecast expected traffic flows. Moreover, a series oftoll-setting experiments would provide LEIA-TR with the data needed tounderstand the incremental changes in traffic patterns due to tollprices, conditional on the current traffic state. The combination ofLEIA-TR's extensive data-collection facilities with state-of-the-artstatistical traffic models will therefore form the basis for a powerfulnew highway traffic control system. Finally it is worth to note that theability to monitor and dynamically notify drivers of present orimpending congestion problems can also provide valuable real time datato traffic reporting bureaus in a manner, which is much more accurateand up-to-date than current aerial based manual observation andrecording.

LEIA-TR's ability to capture and provide for analysis, extremelydetailed traffic flow and congestion data as a function of time wouldprovide extremely valuable statistical data to state and regionalhighway departments and engineers for purposes of augmenting optimalhighway design and expansion planning as well as associated budgeting toaccommodate such needs. Such data may further be useful in detectingdriver behavior patterns at certain points or stretches of roadway whichare suggestive of bed predisposed to future accidents before they occur.Such statistics based informational systems could be used to createcomputer created statistical models of maps at regional or nationalscale revealing traffic flow patterns based upon various desiredanalysis criteria.

Application E. Insurance Analysis, for Automobile Insurance Companies

By pulling together massive amounts of finely detailed information onautomobile drivers and their behaviors, LEIA-TR could be of greatutility to the insurance industry; which relies on high-quality data forthe construction of its pricing models. The data collected by LEIA-TRwill exhibit both a quantity and quality of detail far exceeding thatseen in insurance databases today, allowing for the construction of newgenerations of insurance models and insurance applications.

It should be noted that this example is intended to illustrate, howLEIA-TR's powerful data-collection facilities can be of practical use toa currently existing field. Actuaries have developed a body of specificpricing models to analyze claim data and personal policyholders'characteristics. These models have a double goal: first they aim atselecting the classification variables that are the most powerful inexplaining loss data; then they use the selected variables to establishpremiums for the various levels of coverage. Early models were based onselection techniques of regression analysis, but more specific modelshave been developed lately. The study of these models is part of thecurriculum in most college actuarial programs around the world. In theUnited States, rate-making is the main subject of an examinationadministered by the Casualty Actuarial Society.

An overview of the main pricing models can be found in:

-   -   “Introduction to Ratemaking and Loss Reserving for Property and        Casualty Insurance” Robert Brown, Actex Publications, Winsted,        Conn. 1993    -   “Rate-Making” J. van Eeghen; E. Group and J. Nijssen, Surveys of        Actuarial Studies #2, Nationale Nederlanden, Rotterdam, 1983

a. LEA-TR's Contribution to Auto Insurance: the Collection ofHighly-Detailed Behavioral Data

By its very nature, the LEIA-TR architecture is ideally suited for thecollection of highly-detailed information about motorists—theirvehicles, behaviors, and locales. In this particular implementation,however, LEIA-TR will be operated less for the purposes of real-timeinteraction, and more as a passive data-collection system. However, thequality of this new data will be so precise that it is likely thatentirely new classes of actuarial models will be based on it. Suchmodels could be developed by applying linear as well as non-linear(e.g., kernel regression) analysis techniques to the following:

-   -   identity of driver and number of passengers    -   driver's age, sex, economic class, road behavior, sobriety    -   seat belt and child-seat usage    -   routes traveled (dates, times, speeds, and distances)    -   adverse conditions (weather/road)    -   annual mileage    -   vehicle overnight location    -   accident details (exact point of impact, speed at impact, wheel        movement, effect of braking, etc.)

b. Application to Car Insurance Rate-Setting

The fundamental principle of insurance consists of forming a pool ofpolicyholders. If all risks are not equal to each other, it is fair toask each member of the pool to pay a premium that is proportional to therisk imposed on the pool. The main task of the actuary who sets up a newrating system is to partition the portfolio into homogeneous classes,with all drivers of the same class paying the same premium. The actuaryhas to subdivide the policies according to classification or ratingvariables; for instance, it has been shown in numerous statisticalstudies that young male drivers are more prone to accidents than adultfemales. Consequently age and sex of the main driver are used in mostcountries as rating variables, with heavy surcharges for young males.The identification of significant rating variables is an arduous taskthat uses sophisticated actuarial models based on multivariatestatistical techniques.

The main variables currently used in most US states are

-   -   age, sex, marital status of main driver    -   car model (sports cars pay a hefty surcharge, for instance)    -   use. of car (usually as a function of commuting mileage)    -   territory (overnight location of vehicle).    -   traffic violations and past accidents (over the past three        years)    -   other variables commonly used include good student discount,        multi-car discount, driver training discount, passive seat-belt        discount, etc.

Data provided by LEIA-TR could improve the accuracy of rating in atremendous way, by using the variables that have the best predictivepower. LEIA-TR will revolutionize rating, by enabling insurers to selectthe most efficient rating variables. Currently, insurers have to rely onmany proxy variables, variables that are notoriously inferior in termsof rating efficiency, but that need to be used since the best variablesare either unavailable or subject to fraud. For instance:

1. Commuting mileage is used, as it is fairly easy for insurers to check(even though it is subject to fraud: many policyholders claim they takepublic transportation to work, while in fact they drive.) Commutingmileage is a poor variable, as it fails to take into account pleasuremileage, vacation mileage, use of car during the dangerous night hoursAnnual mileage is a much better predictor of accident behavior thancommuting mileage. Yet insurers are reluctant to use annual mileage, asit is subject to underestimation by policyholders. LEIA-TR could providethat useful information with accuracy, at a cost that would besubstantially less than the cost of an annual physical inspection of thecar's odometer.

2. Sports cars are not dangerous per se. The driver of a sports carusually is. The rationale for the use of sports car as a rating variableis that it is a fairly good proxy for the variable “driver of a sportingnature.” By recording aggressive behavior on the road (maximum speed,accelerations, heavy usage of brakes, etc.) LEIA-TR could correctlyidentify dangerous drivers and tailor the surcharge for aggressive roadbehavior to the true driving pattern of the policyholder. This wouldalso save companies the expense of organizing regular experts' meetingto decide which car model has to be classified as a sports car.

3. Moving traffic violations constitute a very poor evaluation of therespect of the driving code. Orgy a minuscule percentage of effectivevisions of the code lead to a police ticket. Moreover, the collection oftraffic violation data is a very expensive procedure, as it impliesconstant contact between insurers and the state's, Motor Vehicle Agency.LEIA-TR could provide an accurate measure of the respect of the trafficcode by insureds.

4. Discounts for good students or participation in driver training areweak attempts at identifying responsible young drivers. LEIA-TR couldmonitor the road behavior of young drivers much more accurately.

5. Insurers award a discount to cars equipped with passive seat belts,as they have currently no way of checking effective seat ben usage bydrivers. With LEIA-TR, a much better variable (effective usage of seatbelt) than passive seat belts could be used.

6. A current debate concerns the competency of elderly drivers: Althoughdriving ability may degrade with age, it does so at different rates fordifferent drivers, making it impossible to specify a general agethreshold past which people cannot drive. In passively monitoringdriving behavior, LEIA-TR may do an excellent job of extracting thosefeatures related to impaired reflexes, competency, and mental/sensoryawareness. This information could be used to devise a much more accuratemeasure of an elderly driver's ability to drive safely:

7. Insurers currently have no way to measure a driver's likelihood ofdriving under the influence of alcohol. LEIA-TR would be able to conductsuch measurements, using in-car breathalizers or by detecting patternsof driving normally associated by law enforcement officers with DUIs.

These examples show that the data provided by LEIA-TR could be used toanalyze and segment the driving population with a high degree ofaccuracy, allowing the particular risk of an individual driver to beaccurately calculated and pried. Rather than being priced by roughdemographics, an individual's insurance premium would be tailored-madefor his particular situation and driving behavior. Insurance cancersmaking use of the data collection capabilities of LEIA-TR would enjoy atremendous competitive advantage over companies that don't use LEIA-TR.They would be able to use a much more efficient set of classificationvariables, select the better risks, and consequently improveprofitability.

Although a finer-grain classification of drivers and their behaviorswould be of great use to insurance companies, there are substantialregulatory requirements which need to be overcome (on a state-by-statebasis) before such algorithms can be legally used. In light of the needto improve the political acceptability of such methods, an alternativeembodiment of LEIA-TR, which further protects the privacy of individualusers, is presented here.

It is possible to put the pseudonymous proxy server (as described in ourpseudonymous server patent) under the control of a user-side agent,which would also have access to the insurance companies' actuarialmodels. The agent would automatically determine which portions of thedriver's behavioral profile should be forwarded to the insurer, with thegoal of minimizing premium costs to the driver. The informationforwarded by the agent could not be tampered with by the driver,although he would have the option of manually switching off any portionof the information feed he desired. In this model, there is an effect of“incrimination by default” as poor drivers would invariably disclosefewer key components of their profiles. Thus, on a variable-by-variablebasis those drivers disclosing less information to insurers would payhigher premiums than safe drivers willing to disclose their completedriving loge, but in doing so would protect their privacy.

Of course, information of interest to automobile insurance companieswould extend beyond simple driving behavior, and might includeinformation gathered from medical records (e.g. containing informationon drug use/abuse seizure disorders, depression, visual impairment,etc.) or health insurance companies (with whom automobile insurancecompanies might find it advantageous to trade data).

By publicizing the Informal learned in their analyses, automobileinsurance companies could feed advice, on safer driving back toindividual drivers, who would have an incentive to modify theirbehavior. As a driver learned to drive more defensively, his user-sideagent would automatically release more information about his behavior tothe insurance company, gaining him cheaper premiums.

c. Insurance Fraud Detection

LEIA-TR's extensive data collection facilities would be ideal fordetecting (or at least flagging) insurance fraud, as well as cuttingoverall investigation costs. A few examples:

1. Territory is a variable subject to intensive fraud. So manypolicyholders use a fake address in rural South Jersey to avoid thePhiladelphia surcharge that companies hire teams of privateinvestigators to patrol the streets of Philadelphia at night, toidentify cars with New Jersey dense plates. This expensive andinefficient procedure could be eliminated with LEIA-TR's ability torecord location information.

2. In their applications, prospective policyholders have to record thepercentage of use of each car by each member of the family. Usage byyoung drivers tends to be systematically underestimated. LEIA-TR couldefficiently combat this fraudulent technique. For example, a car parkedduring the day at a high school would reveal that the usual driver wasmost likely a teenager, although the family buying the insurance hasregistered the father as being the exclusive driver. One of the ratingdifficulties faced by insurers nowadays is that they do not have acost-effective way to verify who the “main driver” of each car is.LEIA-TR may be an answer to this major problem.

3. The “black boxes” installed in LEIA-TR users' cars record everydetail of a vehicle's workings (speed, direction, force of impact,etc.); this should allow for extremely accurate accidentreconstruction's, which could be decked against accident claims. Thiswould make it easier to correctly attribute liabilities and to gauge thelikelihood of various types of injuries. For example, if the black boxshows that an accident happened at a speed of fifteen miles per hour,claims of whiplash could be successfully contested. As long as a vehicleisn't completely demolished, the sensor devices would continue tomonitor post-accident events, which might also prove important for laterclaims (e.g., if a supposedly immobilized driver is later identifiedpushing the vehicle).

In summary, LEIA-TR promises to be an extremely useful tool in reducinga major problem to insurance companies: information asymmetries. Suchasymmetries, called adverse selection and moral hazard, arise when thecompany has difficulty in assessing the risk of each policyholder.Adverse selection arises from the improper classification of each riskthis cost is transferred to the policyholders in the form of increasedpremiums: a result of imperfect information is that the ‘good’ driversare subsidizing the ‘bad’ drivers. In addition to adverse selection,insurers face a moral hazard externality. The purchase of an insurancecontract reduces the incentive of policyholders to act in a matter thatminimizes the likelihood of a loss. Moral hazard occurs becausepolicyholders act in a way that is unobservable to the company.

LEIA-TR's data collection provides individual-specific information thatwill increase the ability of the insurance company to discern betweenthe drivers' ability and behavior, reducing adverse selection. This willallow the proper categorizing of each risk. Good drivers will berewarded, bad drivers will be penalized. As a result, the properrecognition of driving ability will reduce subsidization of the baddrivers. Their premiums will increase. Consumer monitoring will reduceunobservable behavior, decreasing the effect of moral hazard. In view ofthese decreased externalities, substantial premium reductions willlikely be offered to vehicles that have LEIA-TR.

d. Alternate Embodiments

Although the embodiment described above provides a solution specificallyfor the determination of automobile insurance, LEIA-TR couldsubstantially improve other types of actuarial models.

a) Life/Health Insurance—Certain risky behaviors, such as excessivespeeding, driving under the influence, or, failure to use seatbeltsregularly are factors observed by LEIA-TR which should have a definiteimpact on life and health insurance calculations. It would be possibleto extend LEIA-TR's capabilities so that non-vehicular activities couldbe also detected and analyzed. For example, once a driver has left hisvehicle and moves on foot, LEIA-TR would acquire LIDs from local dial-upexchanges, point-of-sale purchases by credit card, ID badge scans,cell-phones, two-way pagers, hotel and airport transactions, etc.Combining this information (which allows LEIA-TR to track the time andlocation of a pedestrian's route) with information describing localconditions (crime, traffic, and pollution levels), an extremely detailedset of data relevant to health and life Insurance companies could bedeveloped. This archive of information could then be used to gauge thelevel of risk in the full range of an individual's daily behavior, bothinside and outside-his vehicle, allowing the insurance company to setpries accordingly.

b) Aircraft Insurance: As another example, pilot and airplane insurancecould be more accurately priced using panel data sets collected andanalyzed by LEIA-TR. In this mode, LEIA-TR's inputs would include airtraffic control systems. (for location/altitude data), “black boxes”(on-board flight recorders which catalog in high detail the particularoperations of a given airplane), pilot ratings, maintenance records(giving the age and mileage of various components), weather logs, andthe like. In addition to aiding insurance companies, such acomprehensive collection of information would undoubtedly be of use tocompanies in the business of building safer and more reliable airplanes.

Application F: LEIA-TR and Marketing 1) Database Marketing Companies

Some GIS software companies provide geographic mapping softwaresolutions for database marketing companies, see www.caliper.com. Usingthe iReactor technology, it is possible to correlate demographic data ofon-line users with other types of content and purchasing preferences asdemonstrated on-line. These preferences may be either captured on asite-specific basis and/or (ideally) via the preferences elicited by theuser across all sites that the user visits. Given these correlations, itis possible to utilize these demographics to identify on a locationspecific basis the most prevalent content and consumer purchasecharacteristics, of those on-line users which are most similar to thedemographic profile which characterizes that geographic region. As aresult, off-line advertising campaigns can be better targeted.

2) Market Information for Retailers

Using LEIA-TR the complete profile and retail transactional histories ofusers can be determined such that it is possible for retailers to gainaccess to this data to determine the product propensity buying patternsof individuals who browse aisles of their stores, who physically driveor walk past or near their stores or more subtle information such asthose who browse but don't purchase, those who browse, don't purchasebut have a predicted propensity to purchase the types of inventoryselections available, or are average or high-end purchasing customers.This information could be used to allow the retailer to make deductionsabout such things as overall inventory selection, the prominence ofcertain selections in the store, the product or special discountadvertisements that would be most efficient to advertise at the front ofthe store or to pedestrians outside

3) Mapping Commensal. Industrial and Residential. Real Estate MarketOpportunities

A very useful application of LEIA involves the collection of trafficpattern data on a time-specific basis (the day of the week and time ofday) for the traffic, as it passes each piece of real estate. Anelectronic map, which is ideally Web-based, is generated and constantlyupdated based upon this data, which will be useful for assessing thecommercial and residential real estate possibilities of a givenlocation.

Additional information may be provided which may include (but is notlimited to):

a. Origin and destination information of the vehicular traffic (ascaptured by LEIA) which may, especially if correlated with time, suggestthe nature and context of the driver's activities, e.g., rush hourtraffic, errand traffic, etc. It may be useful to factor in the type ofneighborhood the vehicle returns to every night, the type of commercialor business entity she/he drives to work to each day, etc.

b. Other activity-related clues which the user is willing to release,e.g., devices interacted with, content interacted with or transmittedinformation, etc., which may provide insights into the mind-set of whichusers tend to experience when in the vicinity of the real estateproperty.

c. User Profile Data—Aggregate purchase and content affinities as wellas price elasticity data (gleaned from purchase statistics) could bevery useful information to commercial real estate developers andpurveyors. Users with the right profile and a receptive mindset are ofparticular interest.

It should be noted that the present system may be extended toresidential real estate. e.g., what types of jobs (such as quality ofjobs) do local commuters have? What are their numbers? How far do theycommute (particularly if they tend to commute further than the presentreal estate site)? Do their commuting routes tend to pass the currentpotential real estate site? The last three questions would also beparticularly relevant as well for a prospective industrial real estatedevelopment opportunity.

Industrial real estate developers also may be interested in mappedmodels of real estate depicting the professional and known likelyeducational characteristics of the associated local residents in thatregion? What are the other businesses at which they work? (If available)what are their particular positions/responsibilities?

4) Creating Traffic and User Profile Models of Traffic Passing BillboardSites and Providing a Map of Such Information on an Available BillboardSite Basis

The presently described techniques for providing dynamically updatedinformational maps containing detailed statistical data regardingvehicular traffic passing real estate sites can be further extended tosimilar maps of interest to advertisers which contain locations foravailable highway billboards. The present system further providesWeb-based access, which enables advertisers to make reservations andpurchases of such billboards. In one preferred variation, an economic,model is deployed to optimally price the billboards. I.e., a variedrepresentative sample of each type of billboard sharing similartraffic/user profile features with others is auctioned for this purpose.In another variation this on-line auction model is deployed for allbillboards available by the system in order to provide a novel serviceto advertisers which is a “billboard auction” site.

Application G. LEIA-TR and e-Commerce

1) Dynamic Monitoring/Recording of On-Line Traffic Patterns of Visitorsto a Web Site

Real time data mining analysis of Web-wire visitor traffic flowstatistics using traffic flow analysis tools in combination with datamining tools, can reveal (in real time):

a) Where user traffic from the site is coming from, by geographic regionof the POP through which the user (who may be mobile) is accessing thenetwork server. Possibly physical address of the users may be reloadedas well for data aggregation purposes. This information may be of valueto vendors who desire targeting the user with advertiser desiredinformation, i.e., targeting by desired profile features and/or locationcharacteristics with detailed knowledge of what are the typical sitenavigation and/or product purchase characteristics of the userspossessing certain profiles and how do they compare with other visitors.What are the comparative statistics between different types of userprofiles.

b) The sites the user is coming from and going to with regards to thevendor's site. From this data, we can correlate specific geographic andWeb usage characteristics with certain types of actions on that vendor'ssite; e.g., where geographically, do most of the high spending customerswithin product category X come from, from which competitors do most ofthese customers go to, particularly when they stop coming back to thesite (or more generally, what characterizes the behavior of thesewayward customers)? What other sites or content do users with certainproduct consumption characteristics tend to prefer? Real time site usageand product transaction statistics can thus reveal which advertisingcampaigns (on-line or off-line) are most successful (as measured throughreal-time feedback). From which geographic regions (and Internet sites)does each campaign (and media type) elicit the greatest on-line userresponse? These performance statistics can even be measured on a productor category level. It is further of value to consider what are thespending patterns (particularly on competitor sites of certain desirablecustomer segments (e.g., which either represent the profile features ofthat vendor's typical customers or top customers). (Note that such datacould be accessed in aggregate from the ISP by regional POP server oreven wireless capturing data transmissions from the wireless. ISP orcellular provided also pertaining to off-line transactions invariablyfurther requires active cooperation with the user, e.g. at the client ornetwork proxy level and/or his/her transaction processing entities suchas credit cards and bank.

c) In a similar extension of the system, we can provide advertisers witha nationwide overview using GIS software of where, geographically, theirideal target prospects are located. Again off-line campaigns can bebetter targeted and if the products/services of the advertiser arelocation dependent, she/he can set rules to target individuals who arenear the advertiser's physical site or, conversely, near a competitor'ssite (again subject to terms/conditions as negotiated between theadvertiser and customer). If these customers at the point of purchaseare willing to reveal their profiles (such as physical address and siteusage statistics), off-line vendors can better determine where to targetoff-line campaigns and on which sites to deliver their advertising fortheir off-line products/services. What's more, iReactor can measure theimmediate transaction response rate by product as these variouscampaigns are initiated; thus validating and quantifying the directcommercial benefits to the vendor, e.g., geographics, advertising incertain regions or demographics, or sites could be directly correlatedto spikes in sales volumes within X category(s) of the products orservices.

If the user is willing to further reveal real-time anonymized locationdata about themselves, the system is able to identify the particularphysical location(s) which best reach the target audiences as well assubsequently measure the direct effectiveness of these billboardcampaigns, e.g., X percent of users who pass a billboard Y or billboardswith message Z make a related purchase with that vendor.

Why limit marketing performance analysis to a vendor? iReactor canprovide much of this same intelligence about the competition, e.g., whaton-line (or even possibly off-line) advertising messages result intraffic and purchase activities on-line for that competitor.

2) Personalized Search Engine

A personalized search engine which considers the content domains ofinterest to the user and what types of results other individuals mostlike that particular user tend to retrieve and ignore given a particularquery. This data in combination with the search activities of the userenable this system to learn how to better personalize future searchresults for that user. Both the portal and search engine could befurther personalized based upon the physical location user, oralternatively, a physical region(s) manually entered by the user. It isreasonable to allow the browsing interface to enable the user to accessmenus (e.g., heirarchical) site listings, search results or particularURLs and navigate to other sites based upon content similarity, locationsimilarity (or both) or to navigate sites based upon modifying thesecriteria.

3) Personalized Content

Personalized Content Such as Radio or Other Multimedia InformationCustomized by Location of the User—With the advent of increasedbandwidth over wireless networks and decreased cost of local memory, itis certainly an appropriate application to provide the user with morerobust, personalized and location development content, such asmultimedia with video/audio components via the automobiles on boardmultimedia terminals. This content may include advertising, news,weather, relevant privileged news stories or financial information.

A Generalized major portal search and Web browser interface—All of thisinformation may be both multimedia (video and audio) or as desired bythe user, it may preclude strictly an audio component. The system mayconstantly feed information to the user which is automaticallypersonalized, based upon manual search or filtering criteria and/orrelevance to the specific location of the user, as the user comes intophysical proximity to sites or objects of interest. Or in a variation ofthe location filtering features, the user may specify a particularlocation or locations which are of interest manually instead of,passively, receiving information which is relevant to the user's presenttemporal location. Certainly, notification is one mode by which thesystem can be used such as either automatically based upon thatinformation which is automatically measured to be above a certainpredicted threshold of interest to the user or as based upon manualpreference settings. This information may be either location relevant oralternatively may be information delivered via broadcast radio orsatellite TV (e.g., radio news for which storm of high-level personalinterest are delivered to the user on a specifically user designatedchannel(s) or from any channel while the user is in an active listeningmode or while the radio is turned off. (The virtual radio channelconcept was discussed in issued patent “System for the CustomizedElectronic Identification of Desirable Objects”). Radio content whetherbroadcast or unicast could certainly be specifically targeted based uponregional relevance (e.g., categories such as news stories, regional adsfor fine dining, etc., which are regional or highly regionally specificto the present location of the user

4) “What's related” Links

Providing an Alexa-like functionality to the personalized portal. It isreasonable to bias these personalized portal links also by the presentlocation of the user and GIS software can be used to show the geographiclocations of physical sites, vendors, and products associated with thepersonalized location enhanced portal. The parent case also describes anautomated technique based upon the content analysis methods fordetecting and automatically generating links to the pages which are mostrelated to a given page.

5) Location-Specific Web Pages

If desired, as a user browses from site to site, this GIS map could berepresented as a persistent button on the browser display which allowsthe user, with the click of a mouse, to pop up a GIS map, showing thephysical location of the vendor, products/services, or Internet site,geographically as the user views the page. In fact, in a more advancedversion any content which is geographically relevant could berepresented in this manner. Place names in the text can be correlatedwith the associated physical locations on the map. E.g., a news sitesuch as world news could allow the user to observe news events as theyoccur.

6) Personalized Recommendations by Vertical

The iReactor recommender engine can also provide search and filteringcapabilities by particular vertical industries. An example of a.particularly commercially compelling vertical may include a travelportal which allows a user to view travel destinations on a GIS map,zooming on desired (or personally recommended) locations or attractionsand then view multimedia displays of those selections. The presentfunctionality could be usefully integrated into a general automobile (orpedestrian) GIS and navigational system (as described above) enabling(via notification of persistent or selective content feed) delivery oflocation relevant fraud information via the portal.

7) Bargain Finder

The user is browsing a vendor's site with his/her (PDA or stationary PC)and identifies and an exotic fragrance from Victoria's Secret. Thesystem may reveal to the user not only other vendors' which sell thatperfume but also whether or not it is presently in stock, the locationsof these other vendors, (e.g., ranked in ascending order of physicaldistance, price or other feature or purchase/delivery relatedinformation). GIS can then accordingly provide a mapping displayrevealing the geographic locations of the various vendors as listed bythe bargain finder. GIS can further reveal the preferred driving routesfor each vendor given the user's present location (and using GPS) evendynamically instruct the user of. which street or road to follow,dynamically while on route reveal their geographic distances and GISlocation with associated pricing and other relevant buyer information.The system may even be used whereby this functionality quid be providedas a solution for individual sites. The user's PDA is used to scan a barcode or enable the user to input a universal identifier code or name ofa product which the user browses in a store. The system will identifythat product along with price, features, etc. in all other local vendorstores (as well as possibly on-line). What's more, the iReactorrecommender engine could even recommend up-sells, cross-sells and otherproducts which are also liked by. users who prefer that item. Thistechnique (particularly with the per-item inventory availabilityfunction), can be used to direct the user to the most convenientlocation which matches the desired criteria, or in a variation, to alsoreplace a given item when it must be replenished from the user'skitchen. In this way the system can even manage a home maker's shoppinglist. The system may even reveal to each of these vendors detailedinformation including comparative statistics regarding what factors tendto affect the user's purchasing decisions compared with that of thecompetition. Each vendor may then set customized rules which act inresponse to certain user features to better capture buyer loyalty (e.g.,automatically provide counter-offers to combat a competitor's discountsoffer home delivery, etc., or other value added benefits).

An example of a particularly commercially compelling vertical mayinclude a travel portal which allows a user to view travel destinationson a GIS map, zooming on desired (or personally recommended) locationsor attractions and then view mull-media displays of those selections.The present functionality could be usefully integrated into a generalautomobile (or pedestrian) GIS and navigational system (as describedabove).

8) Real-Time Information Delivery to On-Line Shoppers Regarding theLocation and Present Delivery Capability of a Delivery Truck or MailTruck Containing a Desired Object(s)

Co-Pending Patent Application Entitled “Secure Data Interchange”suggests an intriguing and novel application for LEIA-TR in which usersbrowsing an on-line retailer are able to receive updated real timeinformation regarding the present whereabouts of a subset of inventoryselections offered on-line and which are carried by a deliver truck oralternatively a mail truck whose inventory selections are based upon thepreferences of the users who tend to access that site. Along with eachinventory selection which is also carried by a regionally local deliveryor mail truck, the price (which is directly related to the transit timeof the nearest available vehicle carrying that particular selection.Marketing models can be used to determine the optimal price which avendor can request in which pricing parameters may be initially setbased upon an auctioning approach.

4. Conclusion

Although this detailed example has focused on automobiles, the systemdescribed by this patent is relevant and applicable to any and all formsof transportation.

Overall, LEIA-TR offers a novel and useful new approach towardscollecting and making active use of data that is currently being lost inthe day-to-day flow of transport systems.

While the invention has been particularly shown and described withreference to a preferred embodiment, it will be understood by thoseskilled in the art that various changes in form and detail may be madetherein without departing from the spirit and scope of the invention.

What is claimed is:
 1. A method of communicating electronically, themethod comprising: acquiring, at a server, location information for aplurality of mobile devices; identifying, by the server, a particularmobile device of the plurality of mobile devices; utilizing, by theserver, conditional probability to assign a probability to a pluralityof checkpoints around a current location of the particular mobiledevice; identifying, based on the assigned probabilities, a subset ofthe checkpoints corresponding to a plurality of potential futurelocations for said particular mobile device; and pre-caching digitalcontent at the subset of the checkpoints.
 2. The method of claim 1,further comprising: accessing, by the server, a personal profile forsaid particular mobile device; and delivering, by the server, thedigital content to said particular mobile device based upon preferencesidentified in said personal profile, wherein delivering digital contentoccurs when the particular mobile device passes one of said plurality ofpotential future locations.
 3. The method of claim 1, furthercomprising: generating, by the server, a current trip vector based uponthe_acquired location information, wherein the plurality of checkpointsare around the current trip vector, and wherein the digital content fordelivery is further determined based upon preferences identified in saidpersonal profile and said current trip vector.
 4. The method of claim 1,further comprising: acquiring user preference information specifying oneor more preferences in said personal profile, wherein the content isdetermined based at least in part on the user preference information. 5.The method of claim 4, wherein the preferences include allowing thedelivery of the digital content, and wherein the digital content isdetermined to be relevant to the location information.
 6. The method ofclaim 5, wherein the digital content is an advertisement.
 7. The methodof claim 1, further comprising: predicting a time at which theparticular mobile device will pass a first checkpoint of the subset ofthe checkpoints, wherein digital content is pre-cached at the firstcheckpoint based on the predicted time.
 8. The method of claim 7,wherein the prediction of the time is based on current trafficconditions, a projected route, and a speed of the particular mobiledevice.
 9. A system for communicating content to a mobile user using acommunications network, the system comprising: one or more networkservers on the communications network operative to: acquire locationinformation corresponding to the mobile user, utilize, by the server,conditional probability to assign a probability to a plurality ofcheckpoints around a current location of the mobile user, identify,based on the assigned probabilities, a subset of the checkpointscorresponding to a plurality of potential future locations for themobile user, and pre-cache digital content at the subset of thecheckpoints.
 10. The system of claim 9, wherein the one or more networkservers on the communications network are further operative to: access apersonal profile for the mobile user, determine content to deliver tothe mobile user based upon preferences identified in said personalprofile, and communicate the determined content to the mobile user whenthe mobile user passes one of said plurality of potential futurelocations.
 11. The system of claim 9, wherein the one or more networkservers on the communications network are further operative to: generatea current trip vector based upon the acquired location information,wherein the plurality of checkpoints are around the current trip vector,wherein the digital content for delivery is further determined basedupon preferences identified in said personal profile and said currenttrip vector.
 12. The system of claim 9, wherein the one or more networkservers on the communications network are further operative to: acquireuser preference information specifying one or more preferences in saidpersonal profile, wherein the content is determined based at least inpart on the user preference information.
 13. The system of claim 12,wherein the preferences include allowing the delivery of the digitalcontent, and wherein the digital content is determined to be relevant tothe location information.
 14. The system of claim 13, wherein thedigital content is an advertisement.
 15. The system of claim 9, furthercomprising: predicting a time at which the particular mobile device willpass a first checkpoint of the subset of the checkpoints, whereindigital content is pre-cached at the first checkpoint based on thepredicted time.
 16. The system of claim 15, wherein the prediction ofthe time is based on current traffic conditions, a projected route, anda speed of the particular mobile device.
 17. A method of communicatingelectronically, comprising: acquiring, at a server, location informationfor a plurality of mobile devices; identifying, by the server, aparticular mobile device of the plurality of mobile devices; accessing,by the server, a personal profile for said particular mobile device,wherein said personal profile includes a travel route history databaseof the particular mobile device; and delivering, by the server, digitalcontent to said particular mobile device, the digital content fordelivery being determined based upon preferences identified in saidpersonal profile, the location information, and a history of informationrequests from the particular mobile device corresponding to one or moreroutes in said travel route history database.
 18. The method of claim17, further comprising: determining, by the server, a current locationand a predicted future location of said particular mobile device basedon the location information; and determining a corresponding route insaid travel route history database using at least one of: said currentlocation, and said predicted future location.
 19. The method of claim17, further comprising: utilizing autonomous user-side agents tonegotiate terms and conditions for the receipt of an advertisement,wherein digital content is delivered to said particular mobile devicebased upon said negotiated terms and conditions.
 20. The method of claim17, further comprising: determining, by the server, a correspondingroute in said travel route history database using the locationinformation